Cybertwin-Driven Multi-Intelligent Reflecting Surfaces aided Vehicular Edge Computing Leveraged by Deep Reinforcement Learning

Xuhui Zhang1; Huijun Xing1; Weilin Zang2; Zhenzhen Jin3; Yanyan Shen2

10.1109/VTC2022-Fall57202.2022.10012694

2022

IEEE 96th Vehicular Technology Conference (VTC-Fall)

1090-3038

978-1-6654-5469-8

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

1-7

26-29 Sept. 2022

London, United Kingdom

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Recently, the cybertwin-driven intelligent internet of vehicles has received widespread consideration in modern smart cities which makes it possible to run high dimensional, low-latency tolerating, and computational-intensive tasks on the vehicles. Thanks to the development in mobile edge computing, the so-called vehicular edge computing allows mobile vehicles to offload their tasks to the road-side unit or hybrid access point due to the limited computation capability. In this paper, we consider a cybertwin-driven internet of vehicle system that provides computing services for mobile vehicles in local area network or wide area network aided with multi-intelligent reflecting surfaces. Based on this system model, we investigate an optimization problem to jointly maximize the sum of data rate in wide area network, and the sum of energy utilities of vehicles. However, in the proposed system model, it is complicated to design the optimal phase, scheduling and offloading decision policy. To solve this issue, we propose a block coordinate descent and deep reinforcement learning based intelligent IoV computing policy. Numerical results have verified that the proposed algorithm can achieve better IoV computing performance compared with four relative benchmark algorithms.

Vehicular edge computing cybertwin scheduling and offloading intelligent reflecting surface deep reinforcement learning

其他

英语

CPCI-S

Natural Science Foundation of Guangdong Province[2021A1515011856] ; National Natural Science Foundation of China["U1801261","61902388","61503368"] ; Shenzhen Science and Technology Program[JCYJ20190807161805817]

Engineering ; Telecommunications ; Transportation

Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology

WOS:000927580600002

IEEE

1.The Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen, China
2.Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, China
3.Southern University of Science and Technology, Shenzhen, China

Xuhui Zhang,Huijun Xing,Weilin Zang,et al. Cybertwin-Driven Multi-Intelligent Reflecting Surfaces aided Vehicular Edge Computing Leveraged by Deep Reinforcement Learning[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1-7.